Taking a Systems Approach to Adopting AI

Executive Summary

To scale the benefits of AI-innovations, companies need to stop thinking of AI tools and applications — such as natural language processing or computer vision — as standalone solutions. Otherwise, the opportunity cost could be as large as 41% of revenue by 2023. Companies that see AI as components of next-generation enterprise IT systems stand to grow revenues by as much as one-third over the next five years. And as systems evolve, so must the IT workforce. Companies will need multidisciplinary talent that can bridge infrastructure, development tools, programming languages, AI, and machine learning. They’ll also need to combine human talent with a growing army of smart machines to create entirely new kinds of hybrid IT roles. And they’ll need to develop new ways to continuously evolve their workforce, using ongoing learning and organizational transformation to adapt to the relentless pace of systemic AI advances.

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Today, some 80% of large companies have adopted machine learning and other forms of artificial intelligence (AI) in their core business. Five years ago, the figure was less than 10%. Nevertheless, the majority of companies still use AI tools as point solutions — discrete applications, isolated from the wider enterprise IT architecture. That’s what we found in a recent analysis of AI practices at more than 8,300 large, global companies in what we believe is one of the largest-scale studies of enterprise IT systems to date.

To scale the benefits of AI-innovations, those companies need to stop thinking of AI tools and applications — such as natural language processing or computer vision — as standalone solutions. Otherwise, the opportunity cost could be as large as 41% of revenue by 2023. By comparison, leading companies in our research that see AI as components of next-generation enterprise IT systems — what we call “future systems” — stand to grow revenues by as much as one-third over the next five years.

Companies building future systems are harnessing vast amounts of data, ubiquitous computing power, and complementary technologies like cloud, data lakes, 3D printing, the Internet of Things (IoT), and advanced workforce reskilling platforms. And they are implementing AI in a systemic way that captures growth today — but also anticipates change for growth tomorrow. Here’s how your company can do the same:

Reimagine the “IT Stack” for the Age of AI. The conventional IT stack — spanning applications, data, and infrastructure — has reached its practical limit. It simply wasn’t built for today’s complex, ever-changing world containing billions of devices, petabytes of data, and decentralized AI applications scaling for millions of users. Moreover, the conventional computer processing chip is now stretched beyond capacity due to the exponential growth of AI.

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In place of the standalone application, leading companies are starting to reimagine their IT stacks as boundaryless systems of complex machine, employee, consumer, partner, and competitor interconnections. For example, although applications in the cloud may seem like yesterday’s news, cloud-enabled AI with its almost limitless power and elasticity is a mandatory foundation for boundaryless systems. And for most companies, there is still much to do to truly exploit the transformative combinations of AI and cloud services.

Consider how Alibaba Group’s financial arm, Ant Financial, is using the cloud and AI to offer a wide variety of services in mobile payments, banking, insurance, and wealth management. Ant’s cloud uses an open-source platform for automating deployment, scaling, and management of containerized applications. As a result, Ant is able to scale cloud-based AI-driven innovations extensively. For instance, the company has developed an AI system that assesses credit risk in seconds, targeting loans to underserved people who lack bank accounts. Another capability, allowing users to snap a photo after an accident, uses computer vision and algorithms powered by AI to assess the damage, automatically file a claim with the insurer, list nearby repair shops, and estimate repair costs. As Ant has expanded its services, it has automated customer support to help support growth. It has launched an “intelligent assistant”, a voice-recognition system that allows users to buy airline tickets and book hotels by using their voice. The company also recently signed agreements to provide biometric identification and A.I.-enabled risk management systems to several Chinese banks, and it launched an AI-managed service for asset management firms.

Edge computing is also breaking boundaries by moving much of the processing out to the edge of networks, where they meet with the physical world, as with smartphones, robots, drones, security cameras, and IoT. For instance, blockchain company Filament is using data-efficient AI, blockchain, and the Internet of Things (IoT) to enable secure and autonomous edge-computing transactions through a decentralized network stack — independent of underlying infrastructure.

Design for an adaptable IT architecture. As each company’s portfolio of systems and partnerships grows, traditional architectures simply can’t keep pace with the sheer scale of business and technology connections. In their place, adaptable, dynamic architectures, including microservices — simple, discrete services that enable IT modularity — and serverless architectures are fostering new levels of organizational agility and scale.

Consider Nordstrom, an established company with a wealth of valuable data in a fast-changing retail market. They embraced serverless architecture approaches early on, dramatically improving their ability to provide personalized, real-time shopping experiences and to outpace emerging competitors. Their serverless architecture also allows them to dynamically expand and contract in response to real-time retail events associated with a particular product, customer, or salesperson. This in turn allows for far more efficient use of investment capital that would otherwise be tied up in stranded underutilized assets.

Artificial intelligence is a vital part of adaptable systems. Whether it’s virtual agents, natural language processing, machine learning, advanced analytics, or other forms of AI, companies have a host of opportunities to transform the way they do business once their architectures make AI an integral part of the transaction flow. By finding a responsible, transparent balance between human and machine intelligence, and combining it with more basic forms of robotic process automation, adaptable systems can create value in ways that were previously impossible.

To be effective, AI must also gain the trust of the humans it works with. First and foremost, that means being transparent in explaining the decisions and actions AI takes. But it also means allowing humans to step in and take back control when necessary. That’s essential in avoiding any adverse effects on business performance, brand reputation, regulatory compliance, and, above all, human beings themselves.

Systems that can adapt and improve by themselves — such as conversational interfaces that continuously learn new speech patterns — also need vast amounts of trusted data. Data science tools like Jupyter Notebook, a frontend interface for cloud users, help ensure the integrity of data on which adaptable systems depend.

Design systems for humans, by humans. The leading companies in our research recognize that AI now allows them to build systems that talk, listen, see, and understand much the way we do. They know that tomorrow’s advantage will go to those who design systems that adjust to people — not those who continue to expect people to adjust to systems.

Natural conversation and simple touches are already making the keyboard and mouse increasingly obsolete. Voice interaction is now everywhere, from HSBC’s mobile banking app to Apple’s Airpods, letting us bank, dictate documents, or perform numerous other tasks easily without interrupting what we’re doing. And when voice is blended with biometrics, even more possibilities open up. Mizuho Bank, for instance, is working with Sensory and Fujitsu to fuse voice and facial recognition to authenticate users in its mobile banking app. Moreover, when voice and emotion recognition algorithms are used together in customer service, companies can detect when a customer is becoming frustrated with an automated systems and direct the caller to a human agent.

Extended reality — the spectrum of experiences that blur the line between the real world and the simulated world — is also taking hold rapidly. DHL is using augmented reality glasses to display order picking and placement directions for operators, freeing their hands and allowing them to work more efficiently. Extended reality has helped the company boost average productivity by 15% — while also improving accuracy. Now instead of referencing screens to tell them how to organize their work, companies like DHL put it directly in front of their operators field of vision making them more efficient and less likely to create errors as well as improving safety.

Personalization, powered by AI, is at the core of these systems. For example, a large organic grocery store, with an understanding of a customer’s diet preferences (vegan, gluten-free, Paleo, etc.) uses data, AI, voice, and augmented reality to highlight relevant items on the shelf for individual shoppers.

These radically human systems also require new partnerships within and between organizations to support a complex web of user needs. For instance, geriatric care provider Eldercare HomeCare has created a system that integrates the whole support ecosystem of health, medical, financial, food, social, home services, and security partners in one easy-to-access platform, helping the elderly live independently for longer.

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As systems evolve, so must the IT workforce. Companies will need multidisciplinary talent that can bridge infrastructure, development tools, programming languages, AI, and machine learning. They’ll also need to combine human talent with a growing army of smart machines to create entirely new kinds of hybrid IT roles. And they’ll need to develop new ways to continuously evolve their workforce, using ongoing learning and organizational transformation to adapt to the relentless pace of systemic AI advances.

*The authors wish to thank Prashant Shukla, Surya Mukherjee, and David Lavieri for their significant contribution to the research discussed in this article.